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Electronics, Volume 14, Issue 2 (January-2 2025) – 188 articles

Cover Story (view full-size image): Balancing currents between paralleled silicon carbide (SiC) MOSFETs requires innovative approaches, as current balancing techniques proposed by literature cannot autonomously minimize the entire current imbalance from the first operating cycles without requiring the extraction of the device parameters. Also, the effectiveness of most methods has been tested under a symmetrical printed circuit board (PCB) layout. This paper proposes a novel and self-sustaining current balancing approach that balances currents between parallel-connected SiC MOSFETs. The proposed solution actively detects and automatically minimizes the entire current imbalance under both symmetrical and asymmetrical layouts. Additionally, it can be realized without measuring the parameters of SiC MOSFETs. View this paper
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34 pages, 361 KiB  
Article
Model Checking Using Large Language Models—Evaluation and Future Directions
by Sotiris Batsakis, Ilias Tachmazidis, Matthew Mantle, Nikolaos Papadakis and Grigoris Antoniou
Electronics 2025, 14(2), 401; https://doi.org/10.3390/electronics14020401 - 20 Jan 2025
Viewed by 639
Abstract
Large language models (LLMs) such as ChatGPT have risen in prominence recently, leading to the need to analyze their strengths and limitations for various tasks. The objective of this work was to evaluate the performance of large language models for model checking, which [...] Read more.
Large language models (LLMs) such as ChatGPT have risen in prominence recently, leading to the need to analyze their strengths and limitations for various tasks. The objective of this work was to evaluate the performance of large language models for model checking, which is used extensively in various critical tasks such as software and hardware verification. A set of problems were proposed as a benchmark in this work and three LLMs (GPT-4, Claude, and Gemini) were evaluated with respect to their ability to solve these problems. The evaluation was conducted by comparing the responses of the three LLMs with the gold standard provided by model checking tools. The results illustrate the limitations of LLMs in these tasks, identifying directions for future research. Specifically, the best overall performance (ratio of problems solved correctly) was 60%, indicating a high probability of reasoning errors by the LLMs, especially when dealing with more complex scenarios requiring many reasoning steps, and the LLMs typically performed better when generating scripts for solving the problems rather than solving them directly. Full article
(This article belongs to the Special Issue Advances in Information, Intelligence, Systems and Applications)
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29 pages, 5038 KiB  
Article
An Evolutionary Deep Learning Framework for Accurate Remaining Capacity Prediction in Lithium-Ion Batteries
by Yang Liu, Liangyu Han, Yuzhu Wang, Jinqi Zhu, Bo Zhang and Jia Guo
Electronics 2025, 14(2), 400; https://doi.org/10.3390/electronics14020400 - 20 Jan 2025
Viewed by 522
Abstract
Accurate remaining capacity prediction (RCP) of lithium-ion batteries (LIBs) is crucial for ensuring their safety, reliability, and performance, particularly amidst the growing energy crisis and environmental concerns. However, the complex aging processes of LIBs significantly hinder accurate RCP, as traditional prediction methods struggle [...] Read more.
Accurate remaining capacity prediction (RCP) of lithium-ion batteries (LIBs) is crucial for ensuring their safety, reliability, and performance, particularly amidst the growing energy crisis and environmental concerns. However, the complex aging processes of LIBs significantly hinder accurate RCP, as traditional prediction methods struggle to effectively capture nonlinear degradation patterns and long-term dependencies. To tackle these challenges, we introduce an innovative framework that combines evolutionary learning with deep learning for RCP. This framework integrates Temporal Convolutional Networks (TCNs), Bidirectional Gated Recurrent Units (BiGRUs), and an attention mechanism to extract comprehensive time-series features and improve prediction accuracy. Additionally, we introduce a hybrid optimization algorithm that combines the Sparrow Search Algorithm (SSA) with Bayesian Optimization (BO) to enhance the performance of the model. The experimental results validate the superiority of our framework, demonstrating its capability to achieve significantly improved prediction accuracy compared to existing methods. This study provides researchers in battery management systems, electric vehicles, and renewable energy storage with a reliable tool for optimizing lithium-ion battery performance, enhancing system reliability, and addressing the challenges of the new energy industry. Full article
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19 pages, 5395 KiB  
Article
Optimizing 3D Point Cloud Reconstruction Through Integrating Deep Learning and Clustering Models
by Seyyedbehrad Emadi and Marco Limongiello
Electronics 2025, 14(2), 399; https://doi.org/10.3390/electronics14020399 - 20 Jan 2025
Viewed by 512
Abstract
Noise in 3D photogrammetric point clouds—both close-range and UAV-generated—poses a significant challenge to the accuracy and usability of digital models. This study presents a novel deep learning-based approach to improve the quality of point clouds by addressing this issue. We propose a two-step [...] Read more.
Noise in 3D photogrammetric point clouds—both close-range and UAV-generated—poses a significant challenge to the accuracy and usability of digital models. This study presents a novel deep learning-based approach to improve the quality of point clouds by addressing this issue. We propose a two-step methodology: first, a variational autoencoder reduces features, followed by clustering models to assess and mitigate noise in the point clouds. This study evaluates four clustering methods—k-means, agglomerative clustering, Spectral clustering, and Gaussian mixture model—based on photogrammetric parameters, reprojection error, projection accuracy, angles of intersection, distance, and the number of cameras used in tie point calculations. The approach is validated using point cloud data from the Temple of Neptune in Paestum, Italy. The results show that the proposed method significantly improves 3D reconstruction quality, with k-means outperforming other clustering techniques based on three evaluation metrics. This method offers superior versatility and performance compared to traditional and machine learning techniques, demonstrating its potential to enhance UAV-based surveying and inspection practices. Full article
(This article belongs to the Special Issue Point Cloud Data Processing and Applications)
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16 pages, 12452 KiB  
Article
Scaling Nuclear Magnetic Resonance with Integrated Planar Coil and Transceiver Front-End: Co-Design Considerations
by Natachai Terawatsakul, Alireza Saberkari, Yuttapoom Puttisong and Morgan Madec
Electronics 2025, 14(2), 398; https://doi.org/10.3390/electronics14020398 - 20 Jan 2025
Viewed by 471
Abstract
A comprehensive framework for designing a micro-nuclear magnetic resonance (NMR) front-end is presented. Key radio frequency (RF) engineering principles are established to enable efficient excitation and detection of NMR signals. This foundation aims to guide the optimal design of novel handheld NMR devices [...] Read more.
A comprehensive framework for designing a micro-nuclear magnetic resonance (NMR) front-end is presented. Key radio frequency (RF) engineering principles are established to enable efficient excitation and detection of NMR signals. This foundation aims to guide the optimal design of novel handheld NMR devices operating with magnetic fields (B0) below 0.5 Tesla and RF frequencies under 30 MHz. To address the complexities of signal-to-noise ratio optimization in this regime, a specialized metric called the coil performance factor (CPF) is introduced, emphasizing the role of coil design. Through systematic optimization under realistic constraints, an optimal coil configuration maximizing the CPF is identified. This design, with three turns, a coil width of 0.22 mm, and a coil spacing of 0.15 mm, achieves an optimal balance between magnetic field strength, homogeneity, and noise. This work serves as a valuable resource for engineers developing optimized coil designs and RF solutions for handheld NMR devices, providing clear explanations of essential concepts and a practical design methodology. Full article
(This article belongs to the Special Issue RF/MM-Wave Circuits Design and Applications, 2nd Edition)
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24 pages, 7528 KiB  
Article
EOS: Edge-Based Operation Skip Scheme for Real-Time Object Detection Using Viola-Jones Classifier
by Cheol-Ho Choi, Joonhwan Han, Hyun Woo Oh, Jeongwoo Cha and Jungho Shin
Electronics 2025, 14(2), 397; https://doi.org/10.3390/electronics14020397 - 20 Jan 2025
Viewed by 440
Abstract
Machine learning-based object detection systems are preferred due to their cost-effectiveness compared to deep learning approaches. Among machine learning methods, the Viola-Jones classifier stands out for its reasonable accuracy and efficient resource utilization. However, as the number of classification iterations increases or the [...] Read more.
Machine learning-based object detection systems are preferred due to their cost-effectiveness compared to deep learning approaches. Among machine learning methods, the Viola-Jones classifier stands out for its reasonable accuracy and efficient resource utilization. However, as the number of classification iterations increases or the resolution of the input image increases, the detection processing speed may decrease. To address the detection speed issue related to input image resolution, an improved edge component calibration method is applied. Additionally, an edge-based operation skip scheme is proposed to overcome the detection processing speed problem caused by the number of classification iterations. Our experiments using the FDDB public dataset show that our method reduces classification iterations by 24.6157% to 84.1288% compared to conventional methods, except for our previous study. Importantly, our method maintains detection accuracy while reducing classification iterations. This result implies that our method can realize almost real-time object detection when implemented on field-programmable gate arrays. Full article
(This article belongs to the Special Issue Artificial Intelligence in Image and Video Processing)
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16 pages, 1161 KiB  
Article
Multiplex Graph Contrastive Learning with Soft Negatives
by Zhenhao Zhao, Minhong Zhu, Chen Wang, Sijia Wang, Jiqiang Zhang, Li Chen and Weiran Cai
Electronics 2025, 14(2), 396; https://doi.org/10.3390/electronics14020396 - 20 Jan 2025
Viewed by 335
Abstract
Graph Contrastive Learning (GCL) seeks to learn nodal or graph representations that contain maximal consistent information from graph-structured data. While node-level contrasting modes are dominating, some efforts have commenced to explore consistency across different scales. Yet, they tend to lose consistent information and [...] Read more.
Graph Contrastive Learning (GCL) seeks to learn nodal or graph representations that contain maximal consistent information from graph-structured data. While node-level contrasting modes are dominating, some efforts have commenced to explore consistency across different scales. Yet, they tend to lose consistent information and be contaminated by disturbing features. We propose MUX-GCL, a novel cross-scale contrastive learning framework that addresses these key challenges in GCL by leveraging multiplex representations as effective patches to enhance information consistency. Our method introduces a soft-negative contrasting strategy based on positional affinities to reduce false negatives, thereby minimizing information loss during multi-scale contrasts. While this learning mode minimizes contaminating noises, a commensurate contrasting strategy using positional affinities further avoids information loss by correcting false negative pairs across scales. Extensive downstream experiments demonstrate that MUX-GCL yields multiple state-of-the-art results on public datasets. Our theoretical analysis further guarantees the new objective function as a stricter lower bound of mutual information of raw input features and output embeddings, which rationalizes this paradigm. Full article
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26 pages, 23657 KiB  
Article
A Digital Twin Approach for Soil Moisture Measurement with Physically Based Rendering Simulations and Machine Learning
by Ismail Parewai and Mario Köppen
Electronics 2025, 14(2), 395; https://doi.org/10.3390/electronics14020395 - 20 Jan 2025
Viewed by 522
Abstract
Soil is one of the most important factors of agricultural productivity, directly influencing crop growth, water management, and overall yield. However, inefficient soil moisture monitoring methods, such as manual observation and gravimetric in rural areas, often lead to overwatering or underwatering, wasting resources [...] Read more.
Soil is one of the most important factors of agricultural productivity, directly influencing crop growth, water management, and overall yield. However, inefficient soil moisture monitoring methods, such as manual observation and gravimetric in rural areas, often lead to overwatering or underwatering, wasting resources and reduced yields, and harming soil health. This study offers a digital twin approach for soil moisture measurement, integrating real-time physical data, virtual simulations, and machine learning to classify soil moisture conditions. The digital twin is proposed as a virtual representation of physical soil designed to replicate real-world behavior. We used a multispectral rotocam, and high-resolution soil images were captured under controlled conditions. Physically based rendering (PBR) materials were created from these data and implemented in a game engine to simulate soil properties accurately. Image processing techniques were applied to extract key features, followed by machine learning algorithms to classify soil moisture levels (wet, normal, dry). Our results demonstrate that the Soil Digital Twin replicates real-world behavior, with the Random Forest model achieving a high classification accuracy of 96.66% compared to actual soil. This data-driven approach conveys the potential of the Soil Digital Twin to enhance precision farming initiatives and water use efficiency for sustainable agriculture. Full article
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24 pages, 5413 KiB  
Systematic Review
Internet of Things Ontologies for Well-Being, Aging and Health: A Scoping Literature Review
by Hrvoje Belani, Petar Šolić, Eftim Zdravevski and Vladimir Trajkovik
Electronics 2025, 14(2), 394; https://doi.org/10.3390/electronics14020394 - 20 Jan 2025
Viewed by 594
Abstract
Internet of Things aims to simplify and automate complicated tasks by using sensors and other inputs for collecting huge amounts of data, processing them in the cloud and on the edge networks, and allowing decision making toward further interactions via actuators and other [...] Read more.
Internet of Things aims to simplify and automate complicated tasks by using sensors and other inputs for collecting huge amounts of data, processing them in the cloud and on the edge networks, and allowing decision making toward further interactions via actuators and other outputs. As connected IoT devices rank in billions, semantic interoperability remains one of the permanent challenges, where ontologies can provide a great contribution. The main goal of this paper is to analyze the state of research on semantic interoperability in well-being, aging, and health IoT services by using ontologies. This was achieved by analyzing the following research questions: “Which IoT ontologies have been used to implement well-being, aging and health services?” and “What is the dominant approach to achieve semantic interoperability of IoT solutions for well-being, aging and health?’ We conducted a scoping literature review of research papers from 2013 to 2024 by applying the PRISMA-ScR meta-analysis methodology with a custom-built software tool for an exhaustive search through the following digital libraries: IEEE Xplore, PubMed, MDPI, Elsevier ScienceDirect, and Springer Nature Link. By thoroughly analyzing 30 studies from an initial pool of more than 80,000 studies, we conclude that IoT ontologies for well-being, aging, and health services increasingly adopt Semantic Web of Things standards to achieve semantic interoperability by integrating heterogeneous data through unified semantic models. Emerging approaches, like semantic communication, Large Language Models Edge Intelligence, and sustainability-driven IoT analytics, can further enhance service efficiency and promote a holistic “One Well-Being, Aging, and Health” framework. Full article
(This article belongs to the Special Issue Internet of Things for E-health)
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22 pages, 4095 KiB  
Article
A Reliable Routing Algorithm Based on Path Satisfaction in the Energy Internet
by Baoju Liu, Xiangqian Wei, Haifeng Hu, Peng Yu and Lei Shi
Electronics 2025, 14(2), 393; https://doi.org/10.3390/electronics14020393 - 20 Jan 2025
Viewed by 446
Abstract
To meet the ever-increasing strict transmission requirements of services in the Energy Internet (EI), reliable routing algorithms for service are necessary. Most of the existing routing algorithms in the Internet Protocol (IP) layer concentrate on service requirements and network topology features while neglecting [...] Read more.
To meet the ever-increasing strict transmission requirements of services in the Energy Internet (EI), reliable routing algorithms for service are necessary. Most of the existing routing algorithms in the Internet Protocol (IP) layer concentrate on service requirements and network topology features while neglecting spectrum resource utilization in the optical transport layer. The status of spectrum resources in the optical transport layer also affects the availability of the routing path. However, there are few studies that combined service transmission requirements and network structure with spectrum resources of the link. In light of this, it is more practical to design routing algorithms integrated with the IP layer and the optical layer. There are three main innovations as follows: (1) The indicator of path satisfaction is proposed meanwhile the system model and service model are constructed. (2) Searching routing paths for services is abstracted into a constrained optimization problem. The optimal objective is to maximize path satisfaction. At the same time, various service requirements, such as end-to-end latency and bandwidth, should be satisfied. (3) To reduce computational complexity, a heuristic path satisfaction-based service-aware routing algorithm (PSSRA) is designed to resolve it. Extensive experiments are carried out with varied service requests on different network topologies. The final results demonstrate that the proposed algorithm outperforms the existing algorithms regarding the service blocking ratio and service distribution fairness index. Full article
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21 pages, 1171 KiB  
Article
Statistical Analysis of the Sum of Double Random Variables for Security Applications in RIS-Assisted NOMA Networks with a Direct Link
by Sang-Quang Nguyen, Phuong T. Tran, Bui Vu Minh, Tran Trung Duy, Anh-Tu Le, Lubos Rejfek and Lam-Thanh Tu
Electronics 2025, 14(2), 392; https://doi.org/10.3390/electronics14020392 - 20 Jan 2025
Viewed by 577
Abstract
Next- generation wireless communications are projected to integrate reconfigurable intelligent surfaces (RISs) to perpetrate enhanced spectral and energy efficiencies. To quantify the performance of RIS-aided wireless networks, the statistics of a single random variable plus the sum of double random variables becomes a [...] Read more.
Next- generation wireless communications are projected to integrate reconfigurable intelligent surfaces (RISs) to perpetrate enhanced spectral and energy efficiencies. To quantify the performance of RIS-aided wireless networks, the statistics of a single random variable plus the sum of double random variables becomes a core approach to reflect how communication links from RISs improve wireless-based systems versus direct ones. With this in mind, the work applies the statistics of a single random variable plus the sum of double random variables in the secure performance of RIS-based non-orthogonal multi-access (NOMA) systems with the presence of untrusted users. We propose a new communication strategy by jointly considering NOMA encoding and RIS’s phase shift design to enhance the communication of legitimate nodes while degrading the channel capacity of untrusted elements but with sufficient power resources for signal recovery. Following that, we analyze and derive the closed-form expressions of the secrecy effective capacity (SEC) and secrecy outage probability (SOP). All analyses are supported by extensive Monte Carlo simulation outcomes, which facilitate an understanding of system communication behavior, such as the transmit signal-to-noise ratio, the number of RIS elements, the power allocation coefficients, the target data rate of the communication channels, and secure data rate. Finally, the results demonstrate that our proposed communication can be improved significantly with an increase in the number of RIS elements, irrespective of the presence of untrusted proximate or distant users. Full article
(This article belongs to the Special Issue Wireless Sensor Network: Latest Advances and Prospects)
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15 pages, 4180 KiB  
Article
Evaluation Method and Modeling Analysis of the Common Mode Noise Suppression Capability of Full-Bridge Transformers
by Yipeng Kong and Wei Chen
Electronics 2025, 14(2), 391; https://doi.org/10.3390/electronics14020391 - 20 Jan 2025
Viewed by 411
Abstract
The effective capacitance of the common mode port serves as a critical metric for assessing the common mode noise suppression capability of transformers in power converters. Conventionally, the evaluation of transformers in single-ended topologies, such as flyback converters, using a network analyzer necessitates [...] Read more.
The effective capacitance of the common mode port serves as a critical metric for assessing the common mode noise suppression capability of transformers in power converters. Conventionally, the evaluation of transformers in single-ended topologies, such as flyback converters, using a network analyzer necessitates a reference static point and a dynamic point at the transformer port. However, a full-bridge transformer without a center tap lacks a reference static point in both the primary and secondary stages. Consequently, this paper proposes an innovative measurement technique to evaluate the common mode noise suppression capability of full-bridge transformers. This method accounts for the intrinsic parameters of the transformer and refines the high-frequency equivalent circuit model for accurate measurement. Ultimately, the validity of the proposed model is confirmed through experiments conducted on a CLLC converter prototype, offering the industry a straightforward and efficient approach to assessing and testing the common mode noise suppression performance of transformers without a center tap. Full article
(This article belongs to the Section Power Electronics)
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11 pages, 4700 KiB  
Article
Optimized Design and Experimental Study of an Axis-Encircling Beam with Gently Varying Cusp Magnetic Field
by Chaojun Lei, E’Feng Wang, Qixiang Zhao, Shaoliang Shi, Dongshuo Gao, Shufeng Li, Yichi Zhang and Jinjun Feng
Electronics 2025, 14(2), 390; https://doi.org/10.3390/electronics14020390 - 20 Jan 2025
Viewed by 427
Abstract
Mode competition is a significant barrier to advancing gyrotrons towards high frequency, high power, and high efficiency. An axis-encircling beam enables gyrotrons to achieve high interaction efficiency while maintaining stable operation at higher-order harmonics. However, generating a high-quality axis-encircling beam requires an ideal [...] Read more.
Mode competition is a significant barrier to advancing gyrotrons towards high frequency, high power, and high efficiency. An axis-encircling beam enables gyrotrons to achieve high interaction efficiency while maintaining stable operation at higher-order harmonics. However, generating a high-quality axis-encircling beam requires an ideal cusp magnetic field, which is challenging to achieve experimentally. This paper discusses the optimization design of an axis-encircling beam with a gently varying cusp magnetic field. A non-ideal cusp magnetic field is designed using the existing magnetic field and power supply in the laboratory. Under this magnetic field, a large-orbit electronic optical system with 20 kV, 0.5 A, an axis-encircling radius of 3.3 mm at a guiding magnetic field of 0.122 T, and a velocity spread (both transverse and longitudinal) of less than 1.2% was obtained and tested. Full article
(This article belongs to the Section Power Electronics)
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39 pages, 18582 KiB  
Review
Recent Developments in Bidirectional DC-DC Converter Topologies, Control Strategies, and Applications in Photovoltaic Power Generation Systems: A Comparative Review and Analysis
by Ayiguzhali Tuluhong, Zhisen Xu, Qingpu Chang and Tengfei Song
Electronics 2025, 14(2), 389; https://doi.org/10.3390/electronics14020389 - 20 Jan 2025
Viewed by 528
Abstract
As an important piece of equipment in photovoltaic power generation systems, the bidirectional DC-DC converter plays a vital role in improving the conversion efficiency of photovoltaic power generation system. The energy transfer in PV systems heavily relies on efficient bidirectional DC-DC converters. To [...] Read more.
As an important piece of equipment in photovoltaic power generation systems, the bidirectional DC-DC converter plays a vital role in improving the conversion efficiency of photovoltaic power generation system. The energy transfer in PV systems heavily relies on efficient bidirectional DC-DC converters. To ensure stable operation, converters with high reliability and power density are required. This paper introduces the basic principles and topologies of bidirectional DC-DC converters and provides a comparative analysis. And it examines the characteristics of the converters’ control schemes and switching strategies, summarizes the existing research findings and current issues. Finally, it looks ahead to the future development trends of bidirectional DC-DC converters in PV systems. Full article
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19 pages, 2645 KiB  
Article
Power Grid Faults Diagnosis Based on Improved Synchrosqueezing Wavelet Transform and ConvNeXt-v2 Network
by Zhizhong Liu, Zhuo Zhao, Guangyu Huang, Fei Wang, Peng Wang and Jiayue Liang
Electronics 2025, 14(2), 388; https://doi.org/10.3390/electronics14020388 - 20 Jan 2025
Viewed by 390
Abstract
The increasing demand on electrical power consumption all over the world makes the need for stable and reliable electrical power grids is indispensable. Meanwhile, power grid fault diagnosis based on fault recording data is an important technology to ensure the normal operation of [...] Read more.
The increasing demand on electrical power consumption all over the world makes the need for stable and reliable electrical power grids is indispensable. Meanwhile, power grid fault diagnosis based on fault recording data is an important technology to ensure the normal operation of the power grid. Despite the fact that dozens of studies have been put forward to detect electrical faults, these studies still suffer from several downsides, such as fuzzy characteristics of complex fault samples with small inter-class differences and large intra-class differences in different topology structures of distribution networks. To tackle the above issues, this work proposes a power grid fault diagnosis method based on an improved Synchrosqueezing Wavelet Transform (SWT) and ConvNeXt-v2 network (named PGFDSC). Firstly, PGFDSC extracts fault features from the fault recording data with an improved SWT method, and outputs the vector signal to enhance the instantaneous frequency. Then, PGFDSC inputs the extracted feature vectors into the improved ConvNeXt-v2 network for power grid faults recognition. The improved ConvNeXt-v2 network is a self-supervised learning model with the advantages of fast speed and high accuracy, which can effectively solve the problem of inaccurate judgment caused by the high dimensionality of data samples. Finally, extensive experiments were conducted and the experimental results show that PGFDSC improves the accuracy of fault diagnosis by two percentage points compared to other baseline models. Full article
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13 pages, 10090 KiB  
Article
Dual-Band Dual-Circularly Polarized Shared-Aperture Phased Array for S-/C-Band Satellite Communications
by Yuanming Xiao, Lianxing He and Xiaoli Wei
Electronics 2025, 14(2), 387; https://doi.org/10.3390/electronics14020387 - 20 Jan 2025
Viewed by 504
Abstract
In this article, a novel method of achieving a single-layer, dual-band, dual-circularly polarized (CP) shared-aperture phased array antenna with wide beam scanning coverage is presented. The space antenna was designed to provide direct-to-cellular communications services at S-/C-bands with a frequency ratio of 1:1.8. [...] Read more.
In this article, a novel method of achieving a single-layer, dual-band, dual-circularly polarized (CP) shared-aperture phased array antenna with wide beam scanning coverage is presented. The space antenna was designed to provide direct-to-cellular communications services at S-/C-bands with a frequency ratio of 1:1.8. Using novel ceramic substrates with high dielectric constants for antenna miniaturization, the optimum interelement spacing can be ensured in one single layer to meet the large-angle scanning demand. The CP characteristic of the phased array is improved by the sequential rotation technique. A prototype of phased array, which is composed of an 8 × 8 S-band Rx array and a 16 × 16 C-band Tx array, is fabricated to verify this design. The measured results show that the shared-aperture phased array can provide ±50° beam scanning coverage at both the S- and C-bands simultaneously to meet the direct-to-cellular communication demand in low earth orbit (LEO) satellites. Full article
(This article belongs to the Special Issue Antenna Designs for 5G/IoT and Space Applications, 2nd Edition)
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19 pages, 2157 KiB  
Article
Using the Retrieval-Augmented Generation to Improve the Question-Answering System in Human Health Risk Assessment: The Development and Application
by Wenjun Meng, Yuzhe Li, Lili Chen and Zhaomin Dong
Electronics 2025, 14(2), 386; https://doi.org/10.3390/electronics14020386 - 20 Jan 2025
Viewed by 1052
Abstract
While large language models (LLMs) are vital for retrieving relevant information from extensive knowledge bases, they always face challenges, including high costs and issues of credibility. Here, we developed a question answering system focused on human health risk using Retrieval-Augmented Generation (RAG). We [...] Read more.
While large language models (LLMs) are vital for retrieving relevant information from extensive knowledge bases, they always face challenges, including high costs and issues of credibility. Here, we developed a question answering system focused on human health risk using Retrieval-Augmented Generation (RAG). We first proposed a framework to generate question–answer pairs, resulting in 300 high-quality pairs across six subfields. Subsequently, we created both a Naive RAG and an Advanced RAG-based Question-Answering (Q&A) system. Performance evaluation of the 300 question–answer pairs in individual research subfields demonstrated that the Advanced RAG outperformed traditional LLMs (including ChatGPT and ChatGLM) and Naive RAG. Finally, we integrated the developed module for a single subfield to launch a multi-knowledge base question answering system. Our study represents a novel application of RAG technology and LLMs to optimize knowledge retrieval methods in human health risk assessment. Full article
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14 pages, 1305 KiB  
Systematic Review
Role of Virtual Reality in Improving Home Cancer Care: A Systematic Literature Review
by Safa Elkefi and Avishek Choudhury
Electronics 2025, 14(2), 385; https://doi.org/10.3390/electronics14020385 - 20 Jan 2025
Viewed by 661
Abstract
Virtual reality (VR) can play an important role in supporting remote care for cancer patients. The purpose of this study is to explore the possible applications of VR in-home cancer care to support patients and healthcare practitioners. We conducted a systematic literature search [...] Read more.
Virtual reality (VR) can play an important role in supporting remote care for cancer patients. The purpose of this study is to explore the possible applications of VR in-home cancer care to support patients and healthcare practitioners. We conducted a systematic literature search following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched for peer-reviewed publications in PubMed, Web of Science, and IEEE Xplore for research articles published in the last two decades. After the final list of relevant articles was identified, we adopted an inductive approach to categorize and report our findings. We identified 15 relevant research articles and categorized them into three themes: medical treatment, emotional support, and education and training. Six articles leveraged VR to support medical treatment such as outpatient physical therapy and rehabilitation, and pain management. Five used VR to provide emotional support to patients by uplifting their feelings or providing psychological guidance. Lastly, four leveraged VR for education and training purposes. Overall, all studies reported positive outcomes of VR for home cancer care. Our review advocates for VR integration in in-home cancer care. The findings of this review acknowledge the potential of VR in augmenting medical treatment, providing emotional support to patients, and facilitating the education and training of patients and clinicians. VR in-home cancer care can provide patients with a better quality of care. However, just like any other technological integration, VR can also introduce different challenges, including usability, affordability, and acceptance. Therefore, more studies evaluating VR’s usability and acceptance should be conducted. Additionally, stakeholders such as regulatory bodies, patients, and payors should be involved in ensuring VR’s affordability and developing protocols for its safe use. Full article
(This article belongs to the Special Issue Deep Reinforcement Learning and Its Latest Applications)
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19 pages, 2156 KiB  
Article
Low-Delay AES Key Expansion Units Based on DDBT Structure
by Xinxing Zheng, Han Yan, Zhiwei Peng and Xiaoqiang Zhang
Electronics 2025, 14(2), 384; https://doi.org/10.3390/electronics14020384 - 19 Jan 2025
Viewed by 492
Abstract
Advanced Encryption Standard (AES) key expansion unit is usually implemented by chain structure with a long critical path length. That makes key expansion unit become the bottleneck of high-speed AES implementations. In this paper, a design method of low-delay AES key expansion unit [...] Read more.
Advanced Encryption Standard (AES) key expansion unit is usually implemented by chain structure with a long critical path length. That makes key expansion unit become the bottleneck of high-speed AES implementations. In this paper, a design method of low-delay AES key expansion unit is proposed. The proposed design method is based on a delay-drive binary tree (DDBT) structure, which has been proven that it has the shortest critical path length. Based on the proposed design method, a low-delay AES encryption key expansion unit and a low-delay AES encryption/decryption unified key expansion unit are designed in this paper. Both hardware complexity analysis and integrated circuit synthesis indicate that our DDBT-structure-based designs can reduce the delay greatly compared to traditional chain structures. Furthermore, compared to previous works, our designs can achieve the largest throughput. Full article
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32 pages, 8060 KiB  
Article
Study on Robust Path-Tracking Control for an Unmanned Articulated Road Roller Under Low-Adhesion Conditions
by Wei Qiang, Wei Yu, Quanzhi Xu and Hui Xie
Electronics 2025, 14(2), 383; https://doi.org/10.3390/electronics14020383 - 19 Jan 2025
Viewed by 503
Abstract
To enhance the path-tracking accuracy of unmanned articulated road roller (UARR) operating on low-adhesion, slippery surfaces, this paper proposes a hierarchical cascaded control (HCC) architecture integrated with real-time ground adhesion coefficient estimation. Addressing the complex nonlinear dynamics between the two rigid bodies of [...] Read more.
To enhance the path-tracking accuracy of unmanned articulated road roller (UARR) operating on low-adhesion, slippery surfaces, this paper proposes a hierarchical cascaded control (HCC) architecture integrated with real-time ground adhesion coefficient estimation. Addressing the complex nonlinear dynamics between the two rigid bodies of the vehicle and its interaction with the ground, an upper-layer nonlinear model predictive controller (NMPC) is designed. This layer, based on a 4-degree-of-freedom (4-DOF) dynamic model, calculates the required steering torque using position and heading errors. The lower layer employs a second-order sliding mode controller (SOSMC) to precisely track the steering torque and output the corresponding steering wheel angle. To accommodate the anisotropic and time-varying nature of slippery surfaces, a strong-tracking unscented Kalman filter (ST-UKF) observer is introduced for ground adhesion coefficient estimation. By dynamically adjusting the covariance matrix, the observer reduces reliance on historical data while increasing the weight of new data, significantly improving real-time estimation accuracy. The estimated adhesion coefficient is fed back to the upper-layer NMPC, enhancing the control system’s adaptability and robustness under slippery conditions. The HCC is validated through simulation and real-vehicle experiments and compared with LQR and PID controllers. The results demonstrate that HCC achieves the fastest response time and smallest steady-state error on both dry and slippery gravel soil surfaces. Under slippery conditions, while control performance decreases compared to dry surfaces, incorporating ground adhesion coefficient observation reduces steady-state error by 20.62%. Full article
(This article belongs to the Section Electrical and Autonomous Vehicles)
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23 pages, 1147 KiB  
Article
Mutation-Based Approach to Supporting Human–Machine Pair Inspection
by Yujun Dai, Shaoying Liu and Haiyi Liu
Electronics 2025, 14(2), 382; https://doi.org/10.3390/electronics14020382 - 19 Jan 2025
Viewed by 329
Abstract
Human–machine pair inspection refers to a technique that supports programmers and machines working together as a “pair” in source code inspection tasks. The machine provides guidance, while the programmer performs the inspection based on this guidance. Although programmers are often best suited to [...] Read more.
Human–machine pair inspection refers to a technique that supports programmers and machines working together as a “pair” in source code inspection tasks. The machine provides guidance, while the programmer performs the inspection based on this guidance. Although programmers are often best suited to inspect their own code due to familiarity, overconfidence may lead them to overlook important details. This study introduces a novel mutation-based human–machine pair inspection method, which is designed to direct the programmer’s attention to specific code components by applying targeted mutations. We assess the effectiveness of code inspections by analyzing the programmer’s corrections of these mutations. Our approach involves defining mutation operators for each keyword in the program based on historical defects, developing mutation rules based on program keywords and a strategy for automatically generating mutants, and designing a code comparison strategy to quantitatively evaluate code inspection quality. Through a controlled experiment, we demonstrate the effectiveness of mutation-based human–machine pair inspection in aiding programmers during the inspection process. Full article
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19 pages, 1319 KiB  
Article
Towards Failure-Aware Inference in Harsh Operating Conditions: Robust Mobile Offloading of Pre-Trained Neural Networks
by Wenjing Liu, Zhongmin Chen and Yunzhan Gong
Electronics 2025, 14(2), 381; https://doi.org/10.3390/electronics14020381 - 19 Jan 2025
Viewed by 345
Abstract
Pre-trained neural networks like GPT-4 and Llama2 have revolutionized intelligent information processing, but their deployment in industrial applications faces challenges, particularly in harsh environments. To address these related issues, model offloading, which involves distributing the computational load of pre-trained models across edge devices, [...] Read more.
Pre-trained neural networks like GPT-4 and Llama2 have revolutionized intelligent information processing, but their deployment in industrial applications faces challenges, particularly in harsh environments. To address these related issues, model offloading, which involves distributing the computational load of pre-trained models across edge devices, has emerged as a promising solution. While this approach enables the utilization of more powerful models, it faces significant challenges in harsh environments, where reliability, connectivity, and resilience are critical. This paper introduces failure-resilient inference in mobile networks (FRIM), a framework that ensures robust offloading and inference without the need for model retraining or reconstruction. FRIM leverages graph theory to optimize partition redundancy and incorporates an adaptive failure detection mechanism for mobile inference with efficient fault tolerance. Experimental results on DNN models (AlexNet, ResNet, VGG-16) show that FRIM improves inference performance and resilience, enabling more reliable mobile applications in harsh operating environments. Full article
(This article belongs to the Special Issue New Advances in Distributed Computing and Its Applications)
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22 pages, 1818 KiB  
Article
Cooperative Service Caching and Task Offloading in Mobile Edge Computing: A Novel Hierarchical Reinforcement Learning Approach
by Tan Chen, Jiahao Ai, Xin Xiong and Guangwu Hu
Electronics 2025, 14(2), 380; https://doi.org/10.3390/electronics14020380 - 19 Jan 2025
Viewed by 512
Abstract
In the current mobile edge computing (MEC) system, the user dynamics, diversity of applications, and heterogeneity of services have made cooperative service caching and task offloading decision increasingly important. Service caching and task offloading have a naturally hierarchical structure, and thus, hierarchical reinforcement [...] Read more.
In the current mobile edge computing (MEC) system, the user dynamics, diversity of applications, and heterogeneity of services have made cooperative service caching and task offloading decision increasingly important. Service caching and task offloading have a naturally hierarchical structure, and thus, hierarchical reinforcement learning (HRL) can be used to effectively alleviate the dimensionality curse in it. However, traditional HRL algorithms are designed for short-term missions with sparse rewards, while existing HRL algorithms proposed for MEC lack delicate a coupling structure and perform poorly. This article introduces a novel HRL-based algorithm, named hierarchical service caching and task offloading (HSCTO), to solve the problem of the cooperative optimization of service caching and task offloading in MEC. The upper layer of HSCTO makes decisions on service caching while the lower layer is in charge of task offloading strategies. The upper-layer module learns policies by directly utilizing the rewards of the lower-layer agent, and the tightly coupled design guarantees algorithm performance. Furthermore, we adopt a fixed multiple time step method in the upper layer, which eliminates the dependence on the semi-Markov decision processes (SMDPs) theory and reduces the cost of frequent service replacement. We conducted numerical evaluations and the experimental results show that HSCTO improves the overall performance by 20%, and reduces the average energy consumption by 13% compared with competitive baselines. Full article
(This article belongs to the Special Issue Advanced Technologies in Edge Computing and Applications)
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25 pages, 5264 KiB  
Article
Intelligent Gas Risk Assessment and Report Generation for Coal Mines: An Innovative Framework Based on GLM Fine-Tuning
by Yi Sun, Ying Han and Xinke Liu
Electronics 2025, 14(2), 379; https://doi.org/10.3390/electronics14020379 - 19 Jan 2025
Viewed by 456
Abstract
Traditional coal mine gas risk assessment relies on manual operations, leading to inefficiencies, incomplete information integration, and insufficient evaluation accuracy, ultimately affecting safety oversight. This paper proposes an intelligent gas risk assessment and report generation framework (IGRARG) based on fine-tuning a Generative Language [...] Read more.
Traditional coal mine gas risk assessment relies on manual operations, leading to inefficiencies, incomplete information integration, and insufficient evaluation accuracy, ultimately affecting safety oversight. This paper proposes an intelligent gas risk assessment and report generation framework (IGRARG) based on fine-tuning a Generative Language Model (GLM) to address these challenges. The framework integrates multi-source sensor data with the reasoning capabilities of large language models (LLMs). It constructs a gas risk dataset for coal mine safety scenarios, fine-tuned with GLM. Incorporating industry regulations and a domain-specific knowledge base enhanced with a Retrieval-Augmented Generation (RAG) mechanism, the framework automates alarm judgment, suggestion generation, and report creation via a hierarchical graph structure. Real-time human feedback further refines decision making. Experimental results show an evaluation accuracy of 85–93%, with over 300 field tests achieving a 94.46% alarm judgment accuracy and reducing weekly report generation from 90 min to 2–3 min. This framework significantly enhances the intelligence and efficiency of gas risk assessment, providing robust decision support for coal mine safety management. Full article
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51 pages, 10695 KiB  
Article
AI on Wheels: Bibliometric Approach to Mapping of Research on Machine Learning and Deep Learning in Electric Vehicles
by Adrian Domenteanu, Liviu-Adrian Cotfas, Paul Diaconu, George-Aurelian Tudor and Camelia Delcea
Electronics 2025, 14(2), 378; https://doi.org/10.3390/electronics14020378 - 19 Jan 2025
Viewed by 1287
Abstract
The global transition to sustainable energy systems has placed the use of electric vehicles (EVs) among the areas that might contribute to reducing carbon emissions and optimizing energy usage. This paper presents a bibliometric analysis of the interconnected domains of EVs, artificial intelligence [...] Read more.
The global transition to sustainable energy systems has placed the use of electric vehicles (EVs) among the areas that might contribute to reducing carbon emissions and optimizing energy usage. This paper presents a bibliometric analysis of the interconnected domains of EVs, artificial intelligence (AI), machine learning (ML), and deep learning (DL), revealing a significant annual growth rate of 56.4% in research activity. Key findings include the identification of influential journals, authors, countries, and collaborative networks that have driven advancements in this domain. This study highlights emerging trends, such as the integration of renewable energy sources, vehicle-to-grid (V2G) schemes, and the application of AI in EV battery optimization, charging infrastructure, and energy consumption prediction. The analysis also uncovers challenges in addressing information security concerns. By reviewing the top-cited papers, this research underlines the transformative potential of AI-driven solutions in enhancing EV performance and scalability. The results of this study can be useful for practitioners, academics, and policymakers. Full article
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17 pages, 16060 KiB  
Article
Channel-Wise Attention-Enhanced Feature Mutual Reconstruction for Few-Shot Fine-Grained Image Classification
by Qianying Ou and Jinmiao Zou
Electronics 2025, 14(2), 377; https://doi.org/10.3390/electronics14020377 - 19 Jan 2025
Viewed by 355
Abstract
Fine-grained image classification is faced with the challenge of significant intra-class differences and subtle similarities between classes, with a limited number of labelled data. Previous few-shot learning approaches, however, often fail to recognize these discriminative details, such as a bird’s eyes and beak. [...] Read more.
Fine-grained image classification is faced with the challenge of significant intra-class differences and subtle similarities between classes, with a limited number of labelled data. Previous few-shot learning approaches, however, often fail to recognize these discriminative details, such as a bird’s eyes and beak. In this paper, we proposed a channel-wise attention-enhanced feature mutual reconstruction mechanism that helps to alleviate these problems for fine-grained image classification. This mechanism first employed a channel-wise attention module (CAM) to learn the channel weights for both the support and query features. We utilized channel-wise self-attention to assign greater importance to object-relevant channels. This helps the model to focus on subtle yet discriminative details, which is essential to the classification process. Then, we introduce a feature mutual reconstruction module (FMRM) to reconstruct features. The support features are reconstructed by a support-weight-enhanced feature map to reduce the intra-class variations, and query features are reconstructed by a query-weight-enhanced feature map to increase inter-class variations. The results of classification depend on the similarity between reconstructed features and enhanced features. We evaluated the performance based on four fine-grained image datasets when Conv-4 and Resnet-12 were used. The experimental results showed that our method outperforms previous few-shot fine-grained classification methods. This proves that our method can improve fine-grained image classification performance and simultaneously balance both the inter-class and intra-class variations. Full article
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18 pages, 8288 KiB  
Article
Improved Sage–Husa Unscented Kalman Filter for Harmonic State Estimation in Distribution Grid
by Peixuan Yu and Jianjun Sun
Electronics 2025, 14(2), 376; https://doi.org/10.3390/electronics14020376 - 19 Jan 2025
Viewed by 465
Abstract
In distribution grids with the large-scale integration of renewable energy sources and energy storage systems, power signals are often contaminated with time-varying noise and frequency deviation caused by low-frequency inertia. To achieve an accurate dynamic harmonic state estimate (HSE), a novel method based [...] Read more.
In distribution grids with the large-scale integration of renewable energy sources and energy storage systems, power signals are often contaminated with time-varying noise and frequency deviation caused by low-frequency inertia. To achieve an accurate dynamic harmonic state estimate (HSE), a novel method based on an improved Sage–Husa unscented Kalman filter (ISHUKF) is proposed. Considering the frequency deviation, a nonlinear filter model for power signal is proposed, and a UKF is used to address the nonlinear estimation. A Sage–Husa noise estimator is incorporated to enhance the robustness of the UKF-based HSE against the time-varying noise. Additionally, the noise covariance of the Sage–Husa algorithm is modified to ensure the rapid convergence of the estimation. Then, the performance of the proposed method is validated using an IEEE 14-node system. Finally, the method is applied to evaluate the harmonic states of grid-connected inverter faults in real-world scenarios. The simulation and experiment results demonstrate that the proposed method provides an accurate dynamic HSE even in the presence of time-varying noise and frequency deviation. Full article
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6 pages, 138 KiB  
Editorial
Control and Applications of Intelligent Unmanned Aerial Vehicles
by Yunda Yan, Dewei Yi, Hao Lu and Lan Gao
Electronics 2025, 14(2), 375; https://doi.org/10.3390/electronics14020375 - 19 Jan 2025
Viewed by 683
Abstract
Embedding intelligence into the control design of unmanned aerial vehicles (UAVs) has become increasingly critical as these systems are deployed in complex and uncertain environments, often requiring them to adapt dynamically to unpredictable events [...] Full article
(This article belongs to the Special Issue Control and Applications of Intelligent Unmanned Aerial Vehicle)
24 pages, 5134 KiB  
Article
A Novel Data Sanitization Method Based on Dynamic Dataset Partition and Inspection Against Data Poisoning Attacks
by Jaehyun Lee, Youngho Cho, Ryungeon Lee, Simon Yuk, Jaepil Youn, Hansol Park and Dongkyoo Shin
Electronics 2025, 14(2), 374; https://doi.org/10.3390/electronics14020374 - 18 Jan 2025
Viewed by 656
Abstract
Deep learning (DL) technology has shown outstanding performance in various fields such as object recognition and classification, speech recognition, and natural language processing. However, it is well known that DL models are vulnerable to data poisoning attacks, where adversaries modify or inject data [...] Read more.
Deep learning (DL) technology has shown outstanding performance in various fields such as object recognition and classification, speech recognition, and natural language processing. However, it is well known that DL models are vulnerable to data poisoning attacks, where adversaries modify or inject data samples maliciously during the training phase, leading to degraded classification accuracy or misclassification. Since data poisoning attacks keep evolving to avoid existing defense methods, security researchers thoroughly examine data poisoning attack models and devise more reliable and effective detection methods accordingly. In particular, data poisoning attacks can be realistic in an adversarial situation where we retrain a DL model with a new dataset obtained from an external source during transfer learning. By this motivation, we propose a novel defense method that partitions and inspects the new dataset and then removes malicious sub-datasets. Specifically, our proposed method first divides a new dataset into n sub-datasets either evenly or randomly, inspects them by using the clean DL model as a poisoned dataset detector, and finally removes malicious sub-datasets classified by the detector. For partition and inspection, we design two dynamic defensive algorithms: the Sequential Partitioning and Inspection Algorithm (SPIA) and the Randomized Partitioning and Inspection Algorithm (RPIA). With this approach, a resulting cleaned dataset can be used reliably for retraining a DL model. In addition, we conducted two experiments in the Python and DL environment to show that our proposed methods effectively defend against two data poisoning attack models (concentrated poisoning attacks and random poisoning attacks) in terms of various evaluation metrics such as removed poison rate (RPR), attack success rate (ASR), and classification accuracy (ACC). Specifically, the SPIA completely removed all poisoned data under concentrated poisoning attacks in both Python and DL environments. In addition, the RPIA removed up to 91.1% and 99.1% of poisoned data under random poisoning attacks in Python and DL environments, respectively. Full article
(This article belongs to the Special Issue Big Data Analytics and Information Technology for Smart Cities)
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16 pages, 3826 KiB  
Article
Research on Transformer Temperature Early Warning Method Based on Adaptive Sliding Window and Stacking
by Pan Zhang, Qian Zhang, Huan Hu, Huazhi Hu, Runze Peng and Jiaqi Liu
Electronics 2025, 14(2), 373; https://doi.org/10.3390/electronics14020373 - 18 Jan 2025
Viewed by 464
Abstract
This paper proposes a transformer temperature early warning method based on an adaptive sliding window and stacking ensemble learning algorithm, aiming to improve the accuracy and robustness of temperature prediction. The transformer temperature early warning system is crucial for ensuring the safe operation [...] Read more.
This paper proposes a transformer temperature early warning method based on an adaptive sliding window and stacking ensemble learning algorithm, aiming to improve the accuracy and robustness of temperature prediction. The transformer temperature early warning system is crucial for ensuring the safe operation of the power system, and temperature prediction, as the foundation of early warning, directly affects the early warning effectiveness. This paper analyzes the characteristics of transformer temperature using support vector regression, random forest, and gradient boosting regression as base learners and ridge regression as the meta-learner to construct a stacking model. At the same time, Bayesian optimization is used to automatically adjust the sliding window size, achieving adaptive sliding window processing. The experimental results indicate that the temperature prediction method based on adaptive sliding window and stacking significantly reduces prediction errors, enhances the model’s adaptability and generalization ability, and provides more reliable technical support for transformer fault warning. Full article
(This article belongs to the Special Issue Power Electronics in Hybrid AC/DC Grids and Microgrids)
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21 pages, 651 KiB  
Article
A Comparative Study of Incremental ΔΣ Analog-to-Digital Converter Architectures with Extended Order and Resolution
by Monica Aziz, Paul Kaesser, Sameh Ibrahim and Maurits Ortmanns
Electronics 2025, 14(2), 372; https://doi.org/10.3390/electronics14020372 - 18 Jan 2025
Viewed by 621
Abstract
Incremental Delta-Sigma (I-DS) analog-to-digital converters (ADCs) are one of the best candidates for integrated sensor interface systems when it comes to high resolution and power efficiency. Advanced architectures such as Multistage noise shaping (MASH) or extended counting (EC) I-DS ADCs can be used [...] Read more.
Incremental Delta-Sigma (I-DS) analog-to-digital converters (ADCs) are one of the best candidates for integrated sensor interface systems when it comes to high resolution and power efficiency. Advanced architectures such as Multistage noise shaping (MASH) or extended counting (EC) I-DS ADCs can be used to achieve a high resolution and fast conversion times and avoid stability issues. Different architectures have been proposed in the state of the art (SoA), but there exists no extensive quantitative or qualitative comparison between them. This manuscript fills this gap by providing a detailed system-level comparison between MASH, EC, and other architectural options in I-DS ADCs, where different performances between these architectures are realized depending on the employed oversampling ratio (OSR) and the chosen number of quantizer bits. Also, for specific MASH designs, the appropriate choice of the digital filter improves the SQNR. The advantages, disadvantages, and limitations of the different architectures are presented including non-idealities such as coefficient mismatch showing that 2-1 MASH-LI is less sensitive to mismatch and provides a high maximum stable amplitude (MSA) relative to the simulated architectures. Furthermore, the 2-1 EC achieves good results and comes with the advantage of a lower noise penalty factor compared to the MASH architectures. This work is intended to assist designers in selecting the most appropriate enhanced I-DS MASH architecture for their specific requirements and applications. Full article
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